Clinical Trial Data

There are different ways to classify clinical trial data. As mentioned earlier, data can be classified by their physical nature into discrete chunks or as a more continuous measurable quantity. In clinical trials there are other important contextual ways of grouping data as well. For instance, clinical trials are primarily focused on determining two things about a drug, biologic, or device Is it efficacious, and is it safe The data that help to answer these questions are broadly classified as efficacy data and safety data, respectively. 

The Clinical Data Interchange Standards Consortium (CDISC) and its Submission Data Standards group have provided another way to broadly categorize clinical trial data. 

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To assure that the clinical trial data are credible. 

In the clinical setting, zanamivir 12 and oseltamivir 19 are effective in both the prevention and treatment of influenza A and B infection. Benefit in treatment is restricted to patients treated within 48 h of symptom onset (Fleming 2003). Importantly, the effects of drug treatment are a rednction in the severity of illness, and in the incidence of secondary complications. The term of illness is generally rednced between 1 and 2.5 days. The evalnation of zanamivir (Calfee and Hayden 1998 Oxford 2000 Fleming 2003), oseltamivir (Doncette and Aoki 2001 Oxford 2005) and peramivir (Sidwell and Smee 2002) for the treatment, and prophylaxis, of inflnenza virus infection has been reviewed. The reader is directed to these reviews for further details of drug pharmacodynamics and clinical trial data. 

With the increased acceptance of the Internet and the huge innovations in web development tools, web-based data collection and management systems have become the choice of many CROs because of their capability for collecting clinical trial data in real time and disseminating critical clinical trial information to the participating sites and various oversight committees [27]. 

Although 500,000 individuals were enrolled in clinical trials that were submitted to the FDA during 1990-1995 [10], the lack of a repository of clinical trial data, standardized data, and interoperable systems precludes us from efficiently tapping and reanalyzing these data. This missed opportunity underscores the need for standardization and interoperable systems, as discussed above (see Section 27.4.1 on data standards and interoperable systems). 

The Stroke-Thrombolytic Predictive Instrument (Stroke-TPI) has recently been developed in order to provide patient-specific estimates of the probability of a more favorable outcome with rt-PA, and has been proposed as a decision-making aid to patient selection for rt-PA." The estimates from this tool should, however, be treated with caution. The prediction rule is dependent on post hoc mathematical modeling, uses clinical trial data from subjects randomized beyond 3 hours who are not rt-PA-eligible according to FDA labeling and current best practice, and has not been externally validated. It is, therefore, not appropriate to exclude patients from rt-PA treatment based solely on Stroke-TPI predictions. 

Lacunar stroke is characterized by occlusion of a small penetrating artery creating a small deep infarct. Lacunar strokes have the lowest early recurrence risk and best survival rates, but may still cause significant functional morbidity. Although subgroup analyses are available from secondary prevention trials in lacunar stroke, few clinical trial data are available regarding nonthrombolytic antithrombotic therapy for lacunar stroke in the acute setting. 

Although P-blockers should be avoided in patients with decompensated heart failure from left ventricular systolic dysfunction complicating an MI, clinical trial data suggest that it is safe to initiate P-blockers prior to hospital discharge in these patients once heart failure symptoms have resolved.64 These patients may actually benefit more than those without left ventricular dysfunction.65 In patients who cannot tolerate or have a contraindication to a P-blocker, a calcium channel blocker can be used to prevent anginal symptoms, but should not be used routinely in the absence of such symptoms.2,3,62... 

Clinical trial data supporting the use of specialty formulas in niche populations typically are unconvincing in terms of patient outcomes. 

Specialty formulas designed for use in specific clinical situations generally are much more expensive than standard polymeric formulas. Strong clinical trial data supporting use of these specialty formulas in niche populations typically are unconvincing in terms of patient outcomes. 

The SAS code you wrote would eliminate the observation for subjectid=102. This is because the aeyn field is not populated for that row and is therefore eliminated by the WHERE clause in SAS. This is a classic parent-child data problem in clinical trial data, where the parent question is left unanswered but the child response is given. A way to handle this problem would be either to include the aetext field in the WHERE clause or to add a warning to the SAS log. The code in Program 1.4 does both. 

This chapter describes the key clinical data preparation issues and the different classes of clinical data found in clinical trials. Each class of data brings with it a different set of challenges and special handling issues. Sample case report form (CRF) pages are provided with each type of data to aid you in visualizing what the data look like. The key data preparation issues presented are concepts that apply universally across the various classes of clinical trial data. 

Clinical trial data come to the statistical programmer in two basic forms numeric variables and character string (text) variables. With this in mind, there are two considerations for all numeric and text variables. All data should be cleaned if they are needed for analyses, and any data entered asfree-text variables should be coded or categorized if they are needed for analyses. 

Chapter 2 Preparing and Classifying Clinical Trial Data 21... 

Clinical trial data come in two basic forms numeric variables and text variables. Numeric variables are easy for the statistical programmer to handle. Numbers can be analyzed with SAS in a continuous or categorical fashion without much effort. If a numeric variable needs categorization, it is easy enough to categorize the data within SAS. For example, if you had to classify patient age, a simple DATA step such as the following might serve well. 

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